“Grid computing” refers to the concept that applications and resources can be connected in the form of a pervasive network fabric or grid, with grids being viewed as very much analogous to electrical or power grids—accessible everywhere and sharable by everyone. In the general sense, a grid may be defined as a bounded environment (i.e., a collection of networked applications, services, resources, which is treated as a whole and within which grid computing is undertaken). The scope of a grid could range from a small departmental network to a vast collection of resources and services running in multiple locations, spread across the world, and owned by many organizational groups, government bodies, enterprises, or academic institutions.
When exploring the impact of grids within enterprise data centers, the term “enterprise grid” can be used to capture the notion of a grid that is managed by a single entity or business. This is a very specific type of grid, in which there is a clear scope of control and responsibility for managing the grid to meet a specific set of business goals. The extent of an enterprise grid is defined in terms of organizational responsibility and not in terms of geography or asset ownership. Thus, an enterprise grid may span multiple locations or data centers. It may also include applications or services run on behalf of other organizations, such as in an outsourced environment. Enterprise grids must also support various types of workload (transactional, OLTP, batch, compute-intensive, and legacy) and a large, heterogeneous set of resources. This contrasts markedly with more traditional aggregation frameworks in the data center, such as high-availability clusters, load-balanced clusters, or compute-intensive clusters, which are typically focused on a specific application, or type of application, and which are usually deployed on a relatively homogeneous set of resources.